There are two publicly available datasets for books success prediction, namely EMNLP13 [1]} and Goodreads [2]}. The EMNLP13 dataset contains 800 books, while the Goodreads dataset contain 1,003. The main difference between the two datasets is in the definition of success. The success prediction ground-truth of EMNLP13 was based on the book download count on Project Gutenberg, while that of the Goodreads dataset was based on the success of Goodreads book user rating. [2]} argued that download counts are not a good indicator of book success and found about 142 books with different success labels in each dataset, 19.7% of which have more than 100 reviews. We choose to train and test our model using the Goodreads dataset for two reasons. First, it has around 200 more books than the EMNLP13 dataset. Second, the Goodreads dataset gold labels are more credible since the download count, used by EMNLP13, can be seen more as a measure of popularity than success. The books in the Goodreads dataset are from eight different genres and have been rated by at least 10 people. A book is labeled successful if its average Goodreads rating is 3.5 or more (The Goodreads rating scale is 1-5). Otherwise, it is labeled as unsuccessful. Table REF  shows the Goodreads dataset statistics. As shown in the table, the positive (successful) class count is almost double than that of the negative (unsuccessful) class count.
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